__array__(self, dtype: 'npt.DTypeLike | None' = None) -> 'np.ndarray'
Users should not call this directly. Rather, it is invoked by numpy.array
and numpy.asarray
.
The dtype to use for the resulting NumPy array. By default, the dtype is inferred from the data.
The values in the series converted to a numpy.ndarray
with the specified dtype
.
Return the values as a NumPy array.
Series.array
Zero-copy view to the array backing the Series.
Series.to_numpy
Series method for similar behavior.
array
Create a new array from data.
>>> ser = pd.Series([1, 2, 3])
... np.asarray(ser) array([1, 2, 3])
For timezone-aware data, the timezones may be retained with dtype='object'
>>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
... np.asarray(tzser, dtype="object") array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'), Timestamp('2000-01-02 00:00:00+0100', tz='CET')], dtype=object)
Or the values may be localized to UTC and the tzinfo discarded with dtype='datetime64[ns]'
>>> np.asarray(tzser, dtype="datetime64[ns]") # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', ...], dtype='datetime64[ns]')See :
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